Feature discriminability for pattern classification based on neural incremental attribute learning

Abstract

Feature ordering is important in Incremental Attribute Learning where features are gradually trained in one or more size. Apart from time-consuming contribution-based feature ordering methods, feature ordering also can be derived by filter criteria. In this paper, a novel criterion based on a new metric called Discriminability is presented to give ranks for feature ordering. Final results show that the new metric not only is applicable for IAL, but also exhibits better performance in lower error rates. © 2011 Springer-Verlag Berlin Heidelberg.